Author: Tom Kelly
Date: 2025-02-24

# Customising Histogram Plots with Formula Input

#### Tom Kelly

#### 2025-02-24

Since boxplots have become the de facto standard for plotting the distribution of data most users are familiar with these and the formula input for dataframes. However this input is not available in the standard `histoplot` package. Thus it has been restored here for enhanced backwards compatibility with `boxplot`.

As shown below for the `iris` dataset, histogram plots show distribution information taking formula input that `boxplot` implements but `histoplot` is unable to. This demonstrates the customisation demonstrated in the main histoplot vignette using histoplot syntax with the formula method commonly used for `boxplot`, `t.test`, and `lm`.

    library("vioplot")

    data(iris)
    boxplot(Sepal.Length~Species, data = iris)

Whereas performing the same function does not work with `vioplot` (0.2).

    devtools::install_version("vioplot", version = "0.2")
    library("vioplot")
    vioplot(Sepal.Length~Species, data = iris)

    Error in min(data) : invalid 'type' (language) of argument

## Plot Defaults

    vioplot(Sepal.Length~Species, data = iris)

Another concern we see here is that the `vioplot` defaults are not aesthetically pleasing, with a rather glaring colour scheme unsuitable for professional or academic usage. Thus the plot default colours have been changed as shown here:

    vioplot(Sepal.Length~Species, data = iris, main = "Sepal Length")

## Plot colours: Histogram Fill

Plot colours can be further customised as with the original vioplot package using the `col` argument:

    histoplot(Sepal.Length~Species, data = iris, main = "Sepal Length", col="lightblue")

### Vectorisation

However the `vioplot` (0.2) function is unable to colour each histogram separately, thus this is enabled with a vectorised `col` in `histoplot` (0.4):

    histoplot(Sepal.Length~Species, data = iris, main = "Sepal Length", col=c("lightgreen", "lightblue", "palevioletred"))
    legend("topleft", legend=c("setosa", "versicolor", "virginica"), fill=c("lightgreen", "lightblue", "palevioletred"), cex = 0.5)

## Plot colours: Violin Lines and Boxplot

Colours can also be customised for the histogram fill and border separately using the `col` and `border` arguments:

    histoplot(Sepal.Length~Species, data = iris, main = "Sepal Length", col="lightblue", border="royalblue")

Similarly, the arguments `lineCol` and `rectCol` specify the colours of the boxplot outline and rectangle fill. For simplicity the box and whiskers of the boxplot will always have the same colour.

    histoplot(Sepal.Length~Species, data = iris, main = "Sepal Length", rectCol="palevioletred", lineCol="violetred")

The same applies to the colour of the median point with `colMed`:

    histoplot(Sepal.Length~Species, data = iris, main = "Sepal Length", colMed="violet")

### Combined customisation

These can be customised colours can be combined:

    histoplot(Sepal.Length~Species, data = iris, main = "Sepal Length", col="lightblue", border="royalblue", rectCol="palevioletred", lineCol="violetred", colMed="violet")

### Vectorisation

These colour and shape settings can also be customised separately for each histogram:

    histoplot(Sepal.Length~Species, data = iris, main="Sepal Length", col=c("lightgreen", "lightblue", "palevioletred"), border=c("darkolivegreen4", "royalblue4", "violetred4"), rectCol=c("forestgreen", "blue", "palevioletred3"), lineCol=c("darkolivegreen", "royalblue", "violetred4"), colMed=c("green", "cyan", "magenta"), pchMed=c(15, 17, 19))

## Split Bihistogram Plots

We set up the data with two categories (Sepal Width) as follows:

    data(iris)
    summary(iris$Sepal.Width)

    ##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
    ##   2.000   2.800   3.000   3.057   3.300   4.400

    table(iris$Sepal.Width > mean(iris$Sepal.Width))

    ##
    ## FALSE  TRUE
    ##    83    67

    iris_large <- iris[iris$Sepal.Width > mean(iris$Sepal.Width), ]
    iris_small <- iris[iris$Sepal.Width <= mean(iris$Sepal.Width), ]

A direct comparision of 2 datasets can be made with the `side` argument and `add = TRUE` on the second plot:

    histoplot(Sepal.Length~Species, data=iris_large, col = "palevioletred", plotCentre = "line", side = "right")
    histoplot(Sepal.Length~Species, data=iris_small, col = "lightblue", plotCentre = "line", side = "left", add = T)
    title(xlab = "Species", ylab = "Sepal Length")
    legend("topleft", fill = c("lightblue", "palevioletred"), legend = c("small", "large"), title = "Sepal Width")